code: def __call__(self, eval_rs=None, predict=None, label=None, **kwargs):
      metric_result = []
  
      if eval_rs is not None:
          predict, label, input_ = self._parse_input(eval_rs)
  
      for metric in self._metrics:
          rs = metric(predict, label)
          if isinstance(rs, tuple):
              new_rs = [r.to_dict() for r in rs]
              rs = new_rs
          elif isinstance(rs, EvalResult):
              rs = rs.to_dict()
          else:
              raise ValueError(
                  "cannot parse metric result: {}".format(rs)
              )
          metric_result.append(rs)
      return metric_result
  
imports: from typing import Dict
  from transformers import EvalPrediction
  from transformers.trainer_utils import PredictionOutput
  import pandas
  import torch
  import numpy
  from typing import Union
  import json
  import logging
label_desc: Use image node_id 4 for calling the MetricEnsemble obj's underlying member method code with example usage: obj.__call__(eval_rs, predict, label) and returns: metric_result